InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation
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| Authors | Yang Yue et al. |
| Year | 2026 |
| HF Upvotes | 19 |
| arXiv | 2605.14333 |
| Download | |
| Code | https://github.com/LeapLabTHU/InsightTok |
Abstract
Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on discrete tokenization. A central bottleneck is the tokenizer: aggressive downsampling and quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standard discrete-tokenizer objectives being weakly aligned with text legibility and facial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effective discrete visual tokenization framework that enhances text and face fidelity through localized, content-aware perceptual losses. With a compact 16k codebook and a 16x downsampling rate, InsightTok significantly outperforms prior tokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer to autoregressive image generation in InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision in tokenizer training for advancing discrete image generation.
Engineering Breakdown
Plain English
This paper tackles a real problem in autoregressive image generation: text and faces come out garbled because standard discrete tokenizers aggressively compress visual information. The authors propose InsightTok, a tokenizer that uses content-aware, localized compression to preserve fine-grained details where they matter most—keeping text readable and facial features recognizable without sacrificing compression elsewhere in the image.
Key Engineering Insight
The core insight is that one-size-fits-all compression hurts high-detail content like text and faces. By making the tokenizer aware of what it's encoding and applying selective, region-specific quantization, you can maintain fidelity for perceptually critical patterns while still achieving good overall compression ratios.
Why It Matters for Engineers
If you're deploying autoregressive image generators in production, garbage text and distorted faces in outputs are deal-breakers for user-facing applications. This directly improves output quality without requiring architectural changes downstream—it's a tokenizer-level fix that makes your generation pipeline more practical for real products that need readable captions or recognizable people.
Research Context
Prior work on discrete tokenization for generative models focused on generic reconstruction loss, which treats all image regions equally. This paper identifies that text and facial structure need special handling during tokenization, not later in the generation pipeline. It advances the field by showing that better fidelity in these semantic regions is achievable through smarter tokenization design, enabling autoregressive generators to compete with diffusion models on perceptually important details.
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